InstructGPT implements RLHF through several stages, including Supervised Fine-Tuning (SFT), reward model training, and Proximal Policy Optimization (PPO).

The performance of modern reinforcement learning algorithms critically relies on tuning ever increasing numbers of hyperparameters.

Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.

While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g.

Deep model-free reinforcement learning has achieved great successes in recent years, notably in video games [11], board games [19], robotics [10], and challenging control tasks [17,5].